Privacy preserving deep computation model on cloud for big data feature learning +91-9994232214,7806844441, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2016-2017 ----------------------------------- Contact:+91-9994232214,+91-7806844441 Email: ieeeprojectchennai@gmail.com

1. Privacy Preserving Deep Computation Model on Cloud for Big Data Feature
Learning
Abstract:
To improve the efficiency of big data feature learning, the paper proposes a
privacy preserving deep computation model by offloading the expensive
operations to the cloud. Privacy concerns become evident because there are a
large number of private data by various applications in the smart city, such as
sensitive data of governments or proprietary information of enterprises. To
protect the private data, the proposed model uses the BGV encryption scheme to
encrypt the private data and employs cloudservers to perform the high-order
back-propagation algorithm on the encrypted data efficiently for deep
computation model training. Furthermore, the proposed scheme approximates
the Sigmoid function as a polynomial function to support the secure computation
of the activation function with the BGV encryption. In our scheme, only the
encryption operations and the decryption operations are performed by the client
while all the computation tasks are performed on the cloud. Experimental results
show that our scheme is improved by approximately 2.5 times in the training
efficiency compared to the conventional deep computation model without
disclosing the private data using the cloud computingincluding ten nodes. More
importantly, our scheme is highly scalable by employing more cloud servers,
which is particularly suitable for big data.